The technology that is routinely available to neuroradiologists is years or decades behind that which is used in imaging research throughout the world. Current clinical decision-making in neuroradiology is based on information that is insufficient for detecting the subtle changes that presage clinically significant conditions such a a growing tumor, enlarging ventricle, or shrinking hippocampus. Tools that are regularly used in research are not accessible in the clinic due to a number of factors: slow execution speed, sensitivity to scanner manufacturer, field strength and pulse sequence, and lack of FDA clearance. In this project we seek to remove these barriers by further developing FreeSurfer, the world's premier automated brain morphometry package, to (1) integrate novel deep-learning and Random Forest- based patch-matching image synthesis technology into FreeSurfer to make it robust to variations in scanner platform and acquisition parameters, (2) use modern parallel-processing to reduce execution time to a clinically-feasible length, and (3) obtain FDA clearance on the tools allowing them to be routinely used in direct patient care. The resulting product would dramatically improve the information that neuroradiologists have access to for patient diagnosis, staging, and the assessment of the efficacy of therapeutic interventions for disorders including AD, tumor, hydrocephalus, and epilepsy, to name a few.